Outliers detection in non-stationary time-series: Support vector machine versus principal component analysis

This paper aims at comparing two local outliers detection techniques. One is based on a Least Squares Support Vector Machine technique within a sliding window-based learning algorithm. A modification is proposed to improve its performance in non-stationary time-series. The second method relies on the Principal Component Analysis theory along with a robust orthonormal projection approximation subspace tracking with rank-1 modification. The comparative performance of these methods are assessed through simulations using a non-stationary time-series generated with a nonlinear input-output model.

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